Johnny Fernandes
545 lines
24 KiB
Python
545 lines
24 KiB
Python
"""
|
||
2D herding environment for PPO training (Gymnasium-compatible).
|
||
|
||
The dog agent (action: 2D velocity vector) must herd n_sheep into the
|
||
quarantine pen. Sheep dynamics mirror the Webots controller exactly:
|
||
flee (quadratic ramp), separation (inverse-distance), cohesion, wall
|
||
avoidance, and wander.
|
||
|
||
Coordinate system matches the Webots world file:
|
||
field : x ∈ [-15, 15], y ∈ [-15, 15]
|
||
pen : x ∈ [10, 13], y ∈ [-15, -8] (SE corner, open north)
|
||
|
||
Observation (16-dim, fixed regardless of n_sheep):
|
||
dog position (2), flock COM relative to dog (2), top-3 farthest active
|
||
sheep relative to dog (6), pen relative to COM (2), pen relative to
|
||
farthest sheep (2), flock radius (1), fraction penned (1).
|
||
|
||
Permutation-invariant by design: curriculum stages share the same obs dim
|
||
so VecNormalize statistics transfer as n_sheep advances.
|
||
"""
|
||
|
||
import numpy as np
|
||
import gymnasium as gym
|
||
from gymnasium import spaces
|
||
|
||
|
||
class HerdingEnv(gym.Env):
|
||
metadata = {"render_modes": ["human", "rgb_array"], "render_fps": 30}
|
||
|
||
# -----------------------------------------------------------------------
|
||
# World constants — must match Webots world file
|
||
# -----------------------------------------------------------------------
|
||
MAX_SHEEP = 10
|
||
FIELD = 15.0 # half-size; positions ∈ [-FIELD, FIELD]
|
||
PEN_X = (10.0, 13.0)
|
||
PEN_Y = (-15.0, -8.0)
|
||
PEN_CENTER = np.array([11.5, -11.5], dtype=np.float32)
|
||
PEN_ENTRY = np.array([11.5, -8.0], dtype=np.float32) # north entrance face center
|
||
|
||
# -----------------------------------------------------------------------
|
||
# Dynamics — calibrated to match Webots robot specs
|
||
# -----------------------------------------------------------------------
|
||
DOG_SPEED = 2.5 # m/s
|
||
SHEEP_FLEE_V = 0.65 # m/s
|
||
SHEEP_WANDER_V = 0.20 # m/s
|
||
DT = 0.1 # seconds per step
|
||
|
||
# Boid parameters — identical to sheep.py
|
||
FLEE_DIST = 7.0
|
||
SEPARATION_DIST = 2.5
|
||
COHESION_DIST = 8.0
|
||
WALL_MARGIN = 3.5
|
||
|
||
# -----------------------------------------------------------------------
|
||
# Reward weights (simple per-sheep progress — no phases, no gating)
|
||
# -----------------------------------------------------------------------
|
||
W_PER_SHEEP = 2.0 # progress: sum of per-sheep distance-to-pen reductions
|
||
W_ALIGN = 0.05 # gated on action magnitude — dog only earns it when moving.
|
||
# Without gating this created a sit-still trap from n_sheep≥2.
|
||
W_PEN_BONUS = 10.0 # per sheep penned
|
||
W_COMPLETE = 100.0 # all sheep penned
|
||
W_STEP_COST = 0.02 # time penalty — strong enough to punish doing nothing
|
||
W_COMPACT = 0.0 # reward for flock-radius reduction (off by default)
|
||
W_WALL_TOUCH = 0.15 # per-sheep max penalty at wall surface. Linear ramp
|
||
# within WALL_TOUCH_BUFFER gives the RL agent a gradient
|
||
# signal to avoid pinning sheep against pen walls.
|
||
# 0.15 ≈ 7.5× step_cost — strong enough to shape behavior
|
||
# without overwhelming progress reward.
|
||
WALL_TOUCH_BUFFER = 0.8 # metres from wall where penalty starts ramping
|
||
ALIGN_SHAPE = "standoff" # "standoff" (peaks at IDEAL) | "near" (peaks at 0)
|
||
ALIGN_GATED = True # gate alignment on action magnitude
|
||
ENTRY_AWARE = False # When True, targets PEN_ENTRY (entrance face) instead
|
||
# of PEN_CENTER for progress/obs. Intended to fix wall-
|
||
# corralling but collapsed n_sheep≥2 success rate.
|
||
# The wall-touch gradient penalty handles wall avoidance
|
||
# without breaking the core herding signal.
|
||
|
||
# Initial sheep spawn: first sheep placed anywhere; rest within CLUSTER_RADIUS
|
||
# of it. Set to None for legacy uniform-scatter behaviour.
|
||
# Cluster radius ≤ COHESION_DIST (8m) so boid cohesion keeps the flock together.
|
||
INIT_CLUSTER_RADIUS = 5.0
|
||
|
||
def __init__(self, n_sheep: int = 1, max_steps: int = 2000,
|
||
render_mode: str = None, random_n_sheep: bool = False,
|
||
reward_cfg: dict = None):
|
||
super().__init__()
|
||
assert 1 <= n_sheep <= self.MAX_SHEEP
|
||
self.n_sheep = n_sheep
|
||
self.max_steps = max_steps
|
||
self.render_mode = render_mode
|
||
self.random_n_sheep = random_n_sheep # if True, randomise n_sheep each reset
|
||
|
||
# Override class-default reward weights / shape with per-instance config
|
||
# so sweeps can ship configs into subprocess envs via pickled make_env.
|
||
if reward_cfg:
|
||
for k, v in reward_cfg.items():
|
||
if not hasattr(self.__class__, k):
|
||
raise ValueError(f"unknown reward_cfg key: {k}")
|
||
setattr(self, k, v)
|
||
|
||
# Fixed 16-dim observation regardless of n_sheep:
|
||
# dog_pos(2) + rel_com(2) + rel_far1(2) + rel_far2(2) + rel_far3(2)
|
||
# + com_to_pen(2) + far1_to_pen(2) + radius(1) + frac_penned(1)
|
||
self.observation_space = spaces.Box(
|
||
low=-np.inf, high=np.inf, shape=(16,), dtype=np.float32
|
||
)
|
||
|
||
# Action: desired velocity (vx, vy) ∈ [-1, 1]², scaled by DOG_SPEED
|
||
self.action_space = spaces.Box(
|
||
low=-1.0, high=1.0, shape=(2,), dtype=np.float32
|
||
)
|
||
|
||
# Runtime state (populated by reset)
|
||
self._step_count = 0
|
||
self._prev_penned = 0
|
||
self._prev_pen_dist_sum = 0.0
|
||
self.dog_pos = np.zeros(2, dtype=np.float32)
|
||
self.sheep_pos = np.zeros((self.MAX_SHEEP, 2), dtype=np.float32)
|
||
self.penned = np.ones(self.MAX_SHEEP, dtype=bool)
|
||
self.wander_ang = np.zeros(self.MAX_SHEEP, dtype=np.float32)
|
||
|
||
self._fig = None
|
||
|
||
# ------------------------------------------------------------------
|
||
# Curriculum interface
|
||
# ------------------------------------------------------------------
|
||
|
||
def set_n_sheep(self, n: int):
|
||
"""Advance curriculum difficulty; takes effect on next reset()."""
|
||
assert 1 <= n <= self.MAX_SHEEP
|
||
self.n_sheep = n
|
||
|
||
# ------------------------------------------------------------------
|
||
# Gymnasium API
|
||
# ------------------------------------------------------------------
|
||
|
||
def reset(self, seed=None, options=None):
|
||
super().reset(seed=seed)
|
||
self._step_count = 0
|
||
self._prev_penned = 0
|
||
|
||
if self.random_n_sheep:
|
||
self.n_sheep = int(self.np_random.integers(1, self.MAX_SHEEP + 1))
|
||
|
||
# Active sheep (0 .. n_sheep-1): random non-pen positions
|
||
self.sheep_pos[:] = self.PEN_CENTER
|
||
self.penned[:] = True
|
||
|
||
# Spawn first sheep anywhere; subsequent sheep clustered around it
|
||
# so boid cohesion (active within 8m) keeps the flock together.
|
||
# Without clustering, sheep can start 25m apart and never coalesce —
|
||
# task becomes intractable for n_sheep ≥ 2.
|
||
placed = 0
|
||
cluster_center = None
|
||
radius = self.INIT_CLUSTER_RADIUS
|
||
while placed < self.n_sheep:
|
||
if placed == 0 or radius is None:
|
||
p = self.np_random.uniform(-12.0, 12.0, size=(2,)).astype(np.float32)
|
||
else:
|
||
offset = self.np_random.uniform(-radius, radius, size=(2,))
|
||
p = (cluster_center + offset).astype(np.float32)
|
||
p = np.clip(p, -12.0, 12.0)
|
||
if not self._in_pen(p):
|
||
self.sheep_pos[placed] = p
|
||
self.penned[placed] = False
|
||
if placed == 0:
|
||
cluster_center = p.copy()
|
||
placed += 1
|
||
|
||
# Dog: 50% of resets start already behind the flock (anti-pen side,
|
||
# within flee range) to give early training aligned experiences.
|
||
# Use the flock COM as the reference (not sheep[0]) so the bias
|
||
# generalizes from 1-sheep to multi-sheep without putting the dog
|
||
# in front of or inside the flock.
|
||
if self.np_random.random() < 0.5:
|
||
active_pts = self.sheep_pos[:self.n_sheep][~self.penned[:self.n_sheep]]
|
||
ref = active_pts.mean(axis=0) if len(active_pts) else self.sheep_pos[0]
|
||
away = ref - self.PEN_CENTER
|
||
d = float(np.linalg.norm(away))
|
||
if d > 0.1:
|
||
away = away / d
|
||
offset = away * self.np_random.uniform(2.0, self.FLEE_DIST * 0.8)
|
||
self.dog_pos = np.clip(
|
||
(ref + offset).astype(np.float32), -self.FIELD, self.FIELD
|
||
)
|
||
else:
|
||
self.dog_pos = self.np_random.uniform(
|
||
-self.FIELD * 0.8, self.FIELD * 0.8, size=(2,)
|
||
).astype(np.float32)
|
||
|
||
self.wander_ang = self.np_random.uniform(
|
||
-np.pi, np.pi, size=(self.MAX_SHEEP,)
|
||
).astype(np.float32)
|
||
|
||
# Initialise per-sheep pen-distance sum for progress reward
|
||
active = ~self.penned[:self.n_sheep]
|
||
target = self.PEN_ENTRY if self.ENTRY_AWARE else self.PEN_CENTER
|
||
if active.any():
|
||
self._prev_pen_dist_sum = float(
|
||
np.linalg.norm(
|
||
self.sheep_pos[:self.n_sheep][active] - target, axis=1
|
||
).sum()
|
||
)
|
||
com0 = self.sheep_pos[:self.n_sheep][active].mean(axis=0)
|
||
self._prev_radius = float(
|
||
np.linalg.norm(self.sheep_pos[:self.n_sheep][active] - com0, axis=1).max()
|
||
)
|
||
else:
|
||
self._prev_pen_dist_sum = 0.0
|
||
self._prev_radius = 0.0
|
||
|
||
return self._obs(), {}
|
||
|
||
def step(self, action):
|
||
self._step_count += 1
|
||
|
||
act = np.clip(np.asarray(action, dtype=np.float32), -1.0, 1.0)
|
||
old_dog = self.dog_pos.copy()
|
||
new_dog = np.clip(
|
||
self.dog_pos + act * self.DOG_SPEED * self.DT,
|
||
-self.FIELD, self.FIELD
|
||
)
|
||
# Pen wall collision — mirrors Webots geometry. West (x=PEN_X[0]) and
|
||
# east (x=PEN_X[1]) walls block the dog within the pen's y-range.
|
||
# North face (y=PEN_Y[1]=-8) is open. South is the field edge.
|
||
px0, px1 = self.PEN_X
|
||
py0, py1 = self.PEN_Y
|
||
if py0 < new_dog[1] < py1:
|
||
if old_dog[0] < px0 <= new_dog[0]:
|
||
new_dog[0] = px0 - 1e-3
|
||
elif old_dog[0] > px0 >= new_dog[0]:
|
||
new_dog[0] = px0 + 1e-3
|
||
if old_dog[0] > px1 >= new_dog[0]:
|
||
new_dog[0] = px1 + 1e-3
|
||
elif old_dog[0] < px1 <= new_dog[0]:
|
||
new_dog[0] = px1 - 1e-3
|
||
self.dog_pos = new_dog.astype(np.float32)
|
||
|
||
for i in range(self.n_sheep):
|
||
if self.penned[i]:
|
||
continue
|
||
self.sheep_pos[i] = self._step_sheep(i)
|
||
if self._in_pen(self.sheep_pos[i]):
|
||
self.penned[i] = True
|
||
|
||
n_penned = int(self.penned[:self.n_sheep].sum())
|
||
newly_penned = n_penned - self._prev_penned
|
||
self._prev_penned = n_penned
|
||
|
||
reward, rcomps = self._reward(n_penned, newly_penned, act)
|
||
terminated = n_penned == self.n_sheep
|
||
truncated = self._step_count >= self.max_steps
|
||
info = {"n_penned": n_penned, "n_sheep": self.n_sheep,
|
||
"rcomps": rcomps}
|
||
|
||
if self.render_mode == "human":
|
||
self.render()
|
||
|
||
return self._obs(), float(reward), terminated, truncated, info
|
||
|
||
def render(self):
|
||
import matplotlib.pyplot as plt
|
||
import matplotlib.patches as mpatches
|
||
|
||
if self._fig is None:
|
||
plt.ion()
|
||
self._fig, self._ax = plt.subplots(figsize=(6, 6))
|
||
|
||
ax = self._ax
|
||
ax.clear()
|
||
ax.set_xlim(-16, 16); ax.set_ylim(-16, 16)
|
||
ax.set_aspect("equal"); ax.set_facecolor("#dcedc8")
|
||
|
||
ax.add_patch(mpatches.Rectangle(
|
||
(-15, -15), 30, 30, fill=False, edgecolor="#795548", linewidth=2
|
||
))
|
||
pw = self.PEN_X[1] - self.PEN_X[0]
|
||
ph = self.PEN_Y[1] - self.PEN_Y[0]
|
||
ax.add_patch(mpatches.Rectangle(
|
||
(self.PEN_X[0], self.PEN_Y[0]), pw, ph,
|
||
facecolor="#ffe082", edgecolor="#795548", linewidth=2
|
||
))
|
||
ax.text(11.5, -11.5, "pen", ha="center", va="center",
|
||
fontsize=8, color="#795548")
|
||
|
||
com, radius, _ = self._flock_stats()
|
||
ax.add_patch(plt.Circle(com, radius, color="steelblue",
|
||
fill=False, linestyle="--", linewidth=1))
|
||
ax.plot(*com, "+", color="steelblue", markersize=10)
|
||
|
||
for i in range(self.n_sheep):
|
||
if i >= self.n_sheep:
|
||
continue
|
||
color = "deeppink" if self.penned[i] else "white"
|
||
ax.plot(*self.sheep_pos[i], "o", color=color, markersize=11,
|
||
markeredgecolor="#555", markeredgewidth=1.5)
|
||
|
||
ax.plot(*self.dog_pos, "s", color="#4e342e", markersize=13,
|
||
markeredgecolor="black", markeredgewidth=1.5)
|
||
|
||
ax.set_title(
|
||
f"step {self._step_count} | "
|
||
f"penned {int(self.penned[:self.n_sheep].sum())}/{self.n_sheep} | "
|
||
f"r={radius:.1f}m",
|
||
fontsize=11
|
||
)
|
||
self._fig.canvas.draw()
|
||
self._fig.canvas.flush_events()
|
||
plt.pause(0.001)
|
||
|
||
def close(self):
|
||
if self._fig is not None:
|
||
import matplotlib.pyplot as plt
|
||
plt.close(self._fig)
|
||
self._fig = None
|
||
|
||
# ------------------------------------------------------------------
|
||
# Internals
|
||
# ------------------------------------------------------------------
|
||
|
||
def _in_pen(self, pos: np.ndarray) -> bool:
|
||
return (self.PEN_X[0] < pos[0] < self.PEN_X[1] and
|
||
self.PEN_Y[0] < pos[1] < self.PEN_Y[1])
|
||
|
||
def _flock_stats(self):
|
||
"""Return (COM, radius, mean_dispersion) over active sheep."""
|
||
active_mask = ~self.penned[:self.n_sheep]
|
||
if not active_mask.any():
|
||
return self.PEN_CENTER.copy(), 0.0, 0.0
|
||
pts = self.sheep_pos[:self.n_sheep][active_mask]
|
||
com = pts.mean(axis=0)
|
||
dists = np.linalg.norm(pts - com, axis=1)
|
||
return com, float(dists.max()), float(dists.mean())
|
||
|
||
def _obs(self) -> np.ndarray:
|
||
com, radius, _ = self._flock_stats()
|
||
active_mask = ~self.penned[:self.n_sheep]
|
||
|
||
if active_mask.any():
|
||
pts = self.sheep_pos[:self.n_sheep][active_mask]
|
||
dists = np.linalg.norm(pts - com, axis=1)
|
||
sorted_idx = np.argsort(dists)[::-1] # farthest first
|
||
# Top-3 stragglers; pad with COM when fewer active sheep exist
|
||
def nth(n):
|
||
return pts[sorted_idx[n]] if len(sorted_idx) > n else com
|
||
far1, far2, far3 = nth(0), nth(1), nth(2)
|
||
else:
|
||
far1 = far2 = far3 = self.PEN_CENTER.copy()
|
||
|
||
S = self.FIELD
|
||
D = 2 * self.FIELD
|
||
|
||
# far1/far2/far3 expressed relative to COM, not dog.
|
||
# For 1 sheep: far1-COM = far2-COM = far3-COM = [0,0] → cleanly ignorable.
|
||
# For 3+ sheep: non-zero vectors tell the dog where each straggler is
|
||
# within the group, without conflicting with weights trained on 1 sheep.
|
||
# Pen reference for the policy. Aligned with the reward target so the
|
||
# policy isn't forced to learn an implicit offset between what it sees
|
||
# ("pen is here") and what it's rewarded for ("get sheep close to here").
|
||
pen_ref = self.PEN_ENTRY if self.ENTRY_AWARE else self.PEN_CENTER
|
||
return np.array([
|
||
self.dog_pos[0] / S, self.dog_pos[1] / S,
|
||
(com[0] - self.dog_pos[0]) / D, (com[1] - self.dog_pos[1]) / D,
|
||
(far1[0] - com[0]) / D, (far1[1] - com[1]) / D,
|
||
(far2[0] - com[0]) / D, (far2[1] - com[1]) / D,
|
||
(far3[0] - com[0]) / D, (far3[1] - com[1]) / D,
|
||
(pen_ref[0] - com[0]) / D, (pen_ref[1] - com[1]) / D,
|
||
(pen_ref[0] - far1[0]) / D, (pen_ref[1] - far1[1]) / D,
|
||
radius / D,
|
||
active_mask.sum() / self.n_sheep,
|
||
], dtype=np.float32)
|
||
|
||
def _reward(self, n_penned: int, newly_penned: int, action: np.ndarray):
|
||
active = ~self.penned[:self.n_sheep]
|
||
|
||
# Per-sheep progress toward pen: fires whenever any sheep moves closer.
|
||
# Naturally rewards keeping the flock together and pushing toward pen:
|
||
# dog behind flock → all sheep flee toward pen → all contribute positive reward.
|
||
# Dog from wrong side → sheep scatter away from pen → negative reward.
|
||
target = self.PEN_ENTRY if self.ENTRY_AWARE else self.PEN_CENTER
|
||
if active.any():
|
||
pen_dists = np.linalg.norm(
|
||
self.sheep_pos[:self.n_sheep][active] - target, axis=1
|
||
)
|
||
cur_sum = float(pen_dists.sum())
|
||
r_progress = (self._prev_pen_dist_sum - cur_sum) * self.W_PER_SHEEP
|
||
self._prev_pen_dist_sum = cur_sum
|
||
else:
|
||
r_progress = 0.0
|
||
|
||
com, _, _ = self._flock_stats()
|
||
com_dist = float(np.linalg.norm(com - target))
|
||
d_dog_com = float(np.linalg.norm(self.dog_pos - com))
|
||
if d_dog_com > 0.1 and com_dist > 0.1:
|
||
pen_dir = (target - com) / com_dist
|
||
dog_dir = (self.dog_pos - com) / d_dog_com
|
||
cosine = -float(np.dot(pen_dir, dog_dir))
|
||
if self.ALIGN_SHAPE == "standoff":
|
||
IDEAL = 0.5 * (self.SEPARATION_DIST + self.FLEE_DIST)
|
||
HALF = self.FLEE_DIST - IDEAL
|
||
proximity = max(0.0, 1.0 - abs(d_dog_com - IDEAL) / HALF)
|
||
else: # "near"
|
||
proximity = max(0.0, 1.0 - d_dog_com / self.FLEE_DIST)
|
||
move_gate = (min(1.0, float(np.linalg.norm(action)))
|
||
if self.ALIGN_GATED else 1.0)
|
||
alignment = cosine * proximity * move_gate * self.W_ALIGN
|
||
else:
|
||
alignment = 0.0
|
||
|
||
# Wall-touch penalty: distance-based gradient covering all 3 solid pen
|
||
# walls (west, east, south). Linearly ramps from 0 at buffer edge to
|
||
# W_WALL_TOUCH at the wall surface — gives the agent a smooth signal
|
||
# to avoid pinning sheep against walls.
|
||
if self.W_WALL_TOUCH and active.any():
|
||
pts = self.sheep_pos[:self.n_sheep][active]
|
||
px0, px1 = self.PEN_X
|
||
py0, py1 = self.PEN_Y
|
||
buf = self.WALL_TOUCH_BUFFER
|
||
far = buf + 1.0
|
||
d_w = np.where((pts[:, 0] < px0) & (pts[:, 1] > py0) & (pts[:, 1] < py1),
|
||
px0 - pts[:, 0], far)
|
||
d_e = np.where((pts[:, 0] > px1) & (pts[:, 1] > py0) & (pts[:, 1] < py1),
|
||
pts[:, 0] - px1, far)
|
||
d_s = np.where((pts[:, 1] < py0) & (pts[:, 0] > px0) & (pts[:, 0] < px1),
|
||
py0 - pts[:, 1], far)
|
||
d_min = np.minimum(np.minimum(d_w, d_e), d_s)
|
||
penalties = np.maximum(0.0, 1.0 - d_min / buf) * self.W_WALL_TOUCH
|
||
r_wall_touch = -float(penalties.sum())
|
||
else:
|
||
r_wall_touch = 0.0
|
||
|
||
# Compactness shaping: reward decreases in flock radius (active sheep only)
|
||
if self.W_COMPACT and active.any():
|
||
cur_radius = float(np.linalg.norm(
|
||
self.sheep_pos[:self.n_sheep][active] - com, axis=1
|
||
).max())
|
||
r_compact = (self._prev_radius - cur_radius) * self.W_COMPACT
|
||
self._prev_radius = cur_radius
|
||
else:
|
||
r_compact = 0.0
|
||
|
||
r_pen_bonus = newly_penned * self.W_PEN_BONUS
|
||
r_step_cost = -self.W_STEP_COST
|
||
r_complete = self.W_COMPLETE if n_penned == self.n_sheep else 0.0
|
||
reward = (r_progress + alignment + r_compact + r_wall_touch
|
||
+ r_pen_bonus + r_step_cost + r_complete)
|
||
rcomps = {
|
||
"progress": float(r_progress),
|
||
"alignment": float(alignment),
|
||
"compact": float(r_compact),
|
||
"wall_touch": float(r_wall_touch),
|
||
"pen_bonus": float(r_pen_bonus),
|
||
"step_cost": float(r_step_cost),
|
||
"complete": float(r_complete),
|
||
}
|
||
return reward, rcomps
|
||
|
||
def _step_sheep(self, i: int) -> np.ndarray:
|
||
"""Apply one timestep of boid dynamics to sheep i (mirrors sheep.py)."""
|
||
old_pos = self.sheep_pos[i].copy() # saved for pen wall collision check
|
||
pos = old_pos.copy()
|
||
fx, fy = 0.0, 0.0
|
||
fleeing = False
|
||
|
||
# Flee from dog — quadratic ramp
|
||
diff = self.dog_pos - pos
|
||
dist = float(np.linalg.norm(diff))
|
||
if 0.01 < dist < self.FLEE_DIST:
|
||
t = 1.0 - dist / self.FLEE_DIST
|
||
s = t * t * 5.0
|
||
fx -= (diff[0] / dist) * s
|
||
fy -= (diff[1] / dist) * s
|
||
fleeing = True
|
||
|
||
# Separation (inverse-distance) + Cohesion
|
||
cx, cy, cn = 0.0, 0.0, 0
|
||
for j in range(self.n_sheep):
|
||
if j == i or self.penned[j]:
|
||
continue
|
||
dv = self.sheep_pos[j] - pos
|
||
dj = float(np.linalg.norm(dv))
|
||
if 0.3 < dj < self.COHESION_DIST:
|
||
cx += self.sheep_pos[j][0]
|
||
cy += self.sheep_pos[j][1]
|
||
cn += 1
|
||
if 0.05 < dj < self.SEPARATION_DIST:
|
||
push = (self.SEPARATION_DIST - dj) / dj
|
||
fx -= (dv[0] / dj) * push * 2.5
|
||
fy -= (dv[1] / dj) * push * 2.5
|
||
if cn > 0:
|
||
w = 0.08 if fleeing else 0.15
|
||
fx += (cx / cn - pos[0]) * w
|
||
fy += (cy / cn - pos[1]) * w
|
||
|
||
# Wall avoidance
|
||
m, F = self.WALL_MARGIN, self.FIELD
|
||
if pos[0] < -F + m: fx += ((-F + m - pos[0]) / m) * 6.0
|
||
if pos[0] > F - m: fx -= ((pos[0] - (F - m)) / m) * 6.0
|
||
if pos[1] < -F + m: fy += ((-F + m - pos[1]) / m) * 6.0
|
||
if pos[1] > F - m: fy -= ((pos[1] - (F - m)) / m) * 6.0
|
||
|
||
|
||
# Hard-stop clamp: mirrors sheep.py — zero any force driving further
|
||
# into the wall within 0.5 m so the flee force cannot pin the sheep.
|
||
HS = 0.5
|
||
if pos[0] < -F + HS and fx < 0: fx = 0.0
|
||
if pos[0] > F - HS and fx > 0: fx = 0.0
|
||
if pos[1] < -F + HS and fy < 0: fy = 0.0
|
||
if pos[1] > F - HS and fy > 0: fy = 0.0
|
||
|
||
# Wander — suppressed while fleeing
|
||
if not fleeing:
|
||
if self.np_random.random() < 0.02:
|
||
self.wander_ang[i] += float(self.np_random.uniform(-0.6, 0.6))
|
||
fx += float(np.cos(self.wander_ang[i])) * 0.5
|
||
fy += float(np.sin(self.wander_ang[i])) * 0.5
|
||
|
||
# Integrate
|
||
force = np.array([fx, fy])
|
||
mag = float(np.linalg.norm(force))
|
||
if mag > 0.01:
|
||
top_speed = self.SHEEP_FLEE_V if fleeing else self.SHEEP_WANDER_V
|
||
speed = min(top_speed, mag * 0.3)
|
||
pos = np.clip(pos + (force / mag) * speed * self.DT,
|
||
-self.FIELD, self.FIELD)
|
||
|
||
# Pen solid wall collision — mirrors Webots geometry.
|
||
# The pen has THREE solid walls: west (x=PEN_X[0]), east (x=PEN_X[1]),
|
||
# south (y=PEN_Y[0]). The NORTH face (y=PEN_Y[1]=-8) is the open entrance.
|
||
# Sheep may only enter through the north face; crossing a solid wall is blocked.
|
||
px0, px1 = self.PEN_X[0], self.PEN_X[1]
|
||
py0, py1 = self.PEN_Y[0], self.PEN_Y[1]
|
||
entered_from_north = (
|
||
old_pos[1] >= py1 and pos[1] < py1 and px0 < pos[0] < px1
|
||
)
|
||
if not entered_from_north:
|
||
# Block crossing through west wall from outside
|
||
if old_pos[0] < px0 <= pos[0] and py0 < pos[1] < py1:
|
||
pos = np.array([px0 - 1e-3, pos[1]], dtype=np.float32)
|
||
# Block crossing through east wall from outside
|
||
if old_pos[0] > px1 >= pos[0] and py0 < pos[1] < py1:
|
||
pos = np.array([px1 + 1e-3, pos[1]], dtype=np.float32)
|
||
|
||
return pos.astype(np.float32)
|